Python

Introduction to Python
Heavily based on presentations by
Matt Huenerfauth (Penn State)
Guido van Rossum (Google)
Richard P. Muller (Caltech)
...
Monday, October 19, 2009

• Open source general-purpose language.
• Object Oriented, Procedural, Functional
• Easy to interface with C/ObjC/Java/Fortran
• Easy-ish to interface with C++ (via SWIG)
• Great interactive environment
• Downloads: http://www.python.org
• Documentation: http://www.python.org/doc/
• Free book: http://www.diveintopython.org
Python

2.5.x / 2.6.x / 3.x ???
• “Current” version is 2.6.x
• “Mainstream” version is 2.5.x
• The new kid on the block is 3.x
You probably want 2.5.x unless you are starting from
scratch. Then maybe 3.x

Technical Issues
Installing & Running Python

Binaries
• Python comes pre-installed with Mac OS X and
Linux.
• Windows binaries from http://python.org/
• You might not have to do anything!

The Python Interpreter
• Interactive interface to Python
% python
Python 2.5 (r25:51908, May 25 2007, 16:14:04)
[GCC 4.1.2 20061115 (prerelease) (SUSE Linux)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>
• Python interpreter evaluates inputs:
>>> 3*(7+2)
27
• Python prompts with ‘>>>’.
• To exit Python:
• CTRL-D

Running Programs on UNIX
% python filename.py
You could make the *.py file executable and add the
following #!/usr/bin/env python to the top to make it
runnable.

Batteries Included
• Large collection of proven modules included in the
standard distribution.
http://docs.python.org/modindex.html

numpy
• Offers Matlab-ish capabilities within Python
• Fast array operations
• 2D arrays, multi-D arrays, linear algebra etc.
• Downloads: http://numpy.scipy.org/
• Tutorial: http://www.scipy.org/
Tentative_NumPy_Tutorial

matplotlib
• High quality plotting library.
• Downloads: http://matplotlib.sourceforge.net/
#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
mu, sigma = 100, 15
x = mu + sigma*np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green',
alpha=0.75)
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r--', linewidth=1)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$mathrm{Histogram of IQ:} mu=100, sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

PyFITS
• FITS I/O made simple:
• Downloads: http://www.stsci.edu/resources/
software_hardware/pyfits
>>> import pyfits
>>> hdulist = pyfits.open(’input.fits’)
>>> hdulist.info()
Filename: test1.fits
No. Name Type Cards Dimensions Format
0 PRIMARY PrimaryHDU 220 () Int16
1 SCI ImageHDU 61 (800, 800) Float32
>>> hdulist[0].header[’targname’]
’NGC121’
>>> scidata = hdulist[1].data
>>> scidata.shape
(800, 800)
>>> scidata.dtype.name ’float32’
>>> scidata[30:40,10:20] = scidata[1,4] = 999

pyds9 / python-sao
• Interaction with DS9
• Display Python 1-D and 2-D arrays in DS9
• Display FITS files in DS9
• Downloads: Ask Eric Mandel :-)
• Downloads: http://code.google.com/p/python-sao/

Wrappers for Astronomical Packages
• CasaPy (Casa)
• PYGILDAS (GILDAS)
• ParselTongue (AIPS)
• PyRAF (IRAF)
• PyMIDAS (MIDAS)
• PyIMSL (IMSL)

Custom Distributions
• Python(x,y): http://www.pythonxy.com/
• Python(x,y) is a free scientific and engineering development
software for numerical computations, data analysis and data
visualization
• Sage: http://www.sagemath.org/
• Sage is a free open-source mathematics software system
licensed under the GPL. It combines the power of many existing
open-source packages into a common Python-based interface.

Extra Astronomy Links
• iPython (better shell, distributed computing):
http://ipython.scipy.org/
• SciPy (collection of science tools): http://
www.scipy.org/
• Python Astronomy Modules: http://
astlib.sourceforge.net/
• Python Astronomer Wiki: http://macsingularity.org/
astrowiki/tiki-index.php?page=python
• AstroPy: http://www.astro.washington.edu/users/
rowen/AstroPy.html
• Python for Astronomers: http://www.iac.es/
sieinvens/siepedia/pmwiki.php?
n=HOWTOs.EmpezandoPython

The Basics

A Code Sample
x = 34 - 23 # A comment.
y = “Hello” # Another one.
z = 3.45
if z == 3.45 or y == “Hello”:
x = x + 1
y = y + “ World” # String concat.
print x
print y

Enough to Understand the Code
• Assignment uses = and comparison uses ==.
• For numbers + - * / % are as expected.
• Special use of + for string concatenation.
• Special use of % for string formatting (as with printf in C)
• Logical operators are words (and, or, not)
not symbols
• The basic printing command is print.
• The first assignment to a variable creates it.
• Variable types don’t need to be declared.
• Python figures out the variable types on its own.

Basic Datatypes
• Integers (default for numbers)
z = 5 / 2 # Answer is 2, integer division.
• Floats
x = 3.456
• Strings
• Can use “” or ‘’ to specify.
“abc” ‘abc’ (Same thing.)
• Unmatched can occur within the string.
“matt’s”
• Use triple double-quotes for multi-line strings or strings than contain both ‘
and “ inside of them:
“““a‘b“c”””

Whitespace
Whitespace is meaningful in Python: especially
indentation and placement of newlines.
• Use a newline to end a line of code.
• Use when must go to next line prematurely.
• No braces { } to mark blocks of code in Python…
Use consistent indentation instead.
• The first line with less indentation is outside of the block.
• The first line with more indentation starts a nested block
• Often a colon appears at the start of a new block.
(E.g. for function and class definitions.)

Comments
• Start comments with # – the rest of line is ignored.
• Can include a “documentation string” as the first line of any
new function or class that you define.
• The development environment, debugger, and other tools use
it: it’s good style to include one.
def my_function(x, y):
“““This is the docstring. This
function does blah blah blah.”””
# The code would go here...

Assignment
• Binding a variable in Python means setting a name to hold a
reference to some object.
• Assignment creates references, not copies
• Names in Python do not have an intrinsic type. Objects have
types.
• Python determines the type of the reference automatically based on the
data object assigned to it.
• You create a name the first time it appears on the left side of
an assignment expression:
! x = 3
• A reference is deleted via garbage collection after any names
bound to it have passed out of scope.

Accessing Non-Existent Names
• If you try to access a name before it’s been properly created
(by placing it on the left side of an assignment), you’ll get an
error.
>>> y
Traceback (most recent call last):
File "<pyshell#16>", line 1, in -toplevel-
y
NameError: name ‘y' is not defined
>>> y = 3
>>> y
3

Multiple Assignment
• You can also assign to multiple names at the same time.
>>> x, y = 2, 3
>>> x
2
>>> y
3

Naming Rules
• Names are case sensitive and cannot start with a number.
They can contain letters, numbers, and underscores.
bob Bob _bob _2_bob_ bob_2 BoB
• There are some reserved words:
and, assert, break, class, continue, def, del, elif,
else, except, exec, finally, for, from, global, if,
import, in, is, lambda, not, or, pass, print, raise,
return, try, while

Understanding Reference Semantics in
Python

Understanding Reference Semantics
• Assignment manipulates references
—x = y does not make a copy of the object y references
—x = y makes x reference the object y references
• Very useful; but beware!
• Example:
>>> a = [1, 2, 3] # a now references the list [1, 2, 3]
>>> b = a # b now references what a references
>>> a.append(4) # this changes the list a references
>>> print b # if we print what b references,
[1, 2, 3, 4] # SURPRISE! It has changed…
Why??

Understanding Reference Semantics II
• There is a lot going on when we type:
x = 3
• First, an integer 3 is created and stored in memory
• A name x is created
• An reference to the memory location storing the 3 is then
assigned to the name x
• So: When we say that the value of x is 3
• we mean that x now refers to the integer 3
Type: Integer
Data: 3
Name: x
Ref: <address1>
name list memory

Understanding Reference Semantics III
• The data 3 we created is of type integer. In Python, the
datatypes integer, float, and string (and tuple) are
“immutable.”
• This doesn’t mean we can’t change the value of x, i.e. change
what x refers to …
• For example, we could increment x:
>>> x = 3
>>> x = x + 1
>>> print x
4

Understanding Reference Semantics IV
• If we increment x, then what’s really happening is:
1. The reference of name x is looked up.
2. The value at that reference is retrieved.
Type: Integer
Data: 3Name: x
Ref: <address1>
>>> x = x + 1

3. The 3+1 calculation occurs, producing a new data element 4 which is
assigned to a fresh memory location with a new reference.
Type: Integer
Data: 3Name: x
Ref: <address1>
Type: Integer
Data: 4
>>> x = x + 1

4. The name x is changed to point to this new reference.
Type: Integer
Data: 3Name: x
Ref: <address1>
Type: Integer
Data: 4
>>> x = x + 1

4. The name x is changed to point to this new reference.
5. The old data 3 is garbage collected if no name still refers to it.
Name: x
Ref: <address1>
Type: Integer
Data: 4
>>> x = x + 1

Assignment 1
• So, for simple built-in datatypes (integers, floats, strings),
assignment behaves as you would expect:
>>> x = 3 # Creates 3, name x refers to 3
>>> y = x # Creates name y, refers to 3.
>>> y = 4 # Creates ref for 4. Changes y.
>>> print x # No effect on x, still ref 3.
3

Assignment 1
3
Type: Integer
Data: 3
Name: x
Ref: <address1>

Assignment 1
3
Type: Integer
Data: 3
Name: x
Ref: <address1>
Name: y
Ref: <address1>

Assignment 1
3
Type: Integer
Data: 3
Name: x
Ref: <address1>
Type: Integer
Data: 4
Name: y
Ref: <address1>

Assignment 1
3
Type: Integer
Data: 3
Name: x
Ref: <address1>
Type: Integer
Data: 4
Name: y
Ref: <address2>

Assignment 2
• For other data types (lists, dictionaries, user-defined types), assignment
works differently.
• These datatypes are “mutable.”
• When we change these data, we do it in place.
• We don’t copy them into a new memory address each time.
• If we type y=x and then modify y, both x and y are changed.
>>> x = 3 x = some mutable object
>>> y = x y = x
>>> y = 4 make a change to y
>>> print x look at x
3 x will be changed as well
immutable mutable

a
1 2 3
b
a
1 2 3
b
4
a = [1, 2, 3]
a.append(4)
b = a
a 1 2 3
Why? Changing a Shared List

Our surprising example surprising no more...
• So now, here’s our code:
>>> a = [1, 2, 3] # a now references the list [1, 2, 3]
>>> b = a # b now references what a references
>>> a.append(4) # this changes the list a references
>>> print b # if we print what b references,
[1, 2, 3, 4] # SURPRISE! It has changed…

Sequence types:
Tuples, Lists, and Strings

Sequence Types
1. Tuple
• A simple immutable ordered sequence of items
• Items can be of mixed types, including collection types
2. Strings
• Immutable
• Conceptually very much like a tuple
3. List
• Mutable ordered sequence of items of mixed types

Similar Syntax
• All three sequence types (tuples, strings, and lists)
share much of the same syntax and functionality.
• Key difference:
• Tuples and strings are immutable
• Lists are mutable
• The operations shown in this section can be
applied to all sequence types
• most examples will just show the operation
performed on one

Sequence Types 1
• Tuples are defined using parentheses (and commas).
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
• Lists are defined using square brackets (and commas).
>>> li = [“abc”, 34, 4.34, 23]
• Strings are defined using quotes (“, ‘, or “““).
>>> st = “Hello World”
>>> st = ‘Hello World’
>>> st = “““This is a multi-line
string that uses triple quotes.”””

Sequence Types 2
• We can access individual members of a tuple, list, or string
using square bracket “array” notation.
• Note that all are 0 based…
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> tu[1] # Second item in the tuple.
‘abc’
>>> li = [“abc”, 34, 4.34, 23]
>>> li[1] # Second item in the list.
34
>>> st = “Hello World”
>>> st[1] # Second character in string.
‘e’

Positive and negative indices
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Positive index: count from the left, starting with 0.
>>> t[1]
‘abc’
Negative lookup: count from right, starting with –1.
>>> t[-3]
4.56

Slicing: Return Copy of a Subset 1
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Return a copy of the container with a subset of the original
members. Start copying at the first index, and stop copying
before the second index.
>>> t[1:4]
(‘abc’, 4.56, (2,3))
You can also use negative indices when slicing.
>>> t[1:-1]
(‘abc’, 4.56, (2,3))

Slicing: Return Copy of a Subset 2
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Omit the first index to make a copy starting from the beginning
of the container.
>>> t[:2]
(23, ‘abc’)
Omit the second index to make a copy starting at the first index
and going to the end of the container.
>>> t[2:]
(4.56, (2,3), ‘def’)

Copying the Whole Sequence
To make a copy of an entire sequence, you can use [:].
>>> t[:]
(23, ‘abc’, 4.56, (2,3), ‘def’)
Note the difference between these two lines for mutable
sequences:
>>> list2 = list1 # 2 names refer to 1 ref
# Changing one affects both
>>> list2 = list1[:] # Two independent copies, two refs

The ‘in’ Operator
• Boolean test whether a value is inside a container:
>>> t = [1, 2, 4, 5]
>>> 3 in t
False
>>> 4 in t
True
>>> 4 not in t
False
• For strings, tests for substrings
>>> a = 'abcde'
>>> 'c' in a
True
>>> 'cd' in a
True
>>> 'ac' in a
False
• Be careful: the in keyword is also used in the syntax of
for loops and list comprehensions.

The + Operator
• The + operator produces a new tuple, list, or string whose
value is the concatenation of its arguments.
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
>>> “Hello” + “ ” + “World”
‘Hello World’

The * Operator
• The * operator produces a new tuple, list, or string that
“repeats” the original content.
>>> (1, 2, 3) * 3
(1, 2, 3, 1, 2, 3, 1, 2, 3)
>>> [1, 2, 3] * 3
[1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> “Hello” * 3
‘HelloHelloHello’

Mutability:
Tuples vs. Lists

Tuples: Immutable
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> t[2] = 3.14
Traceback (most recent call last):
File "<pyshell#75>", line 1, in -toplevel-
tu[2] = 3.14
TypeError: object doesn't support item assignment
You can’t change a tuple.
You can make a fresh tuple and assign its reference to a previously used
name.
>>> t = (23, ‘abc’, 3.14, (2,3), ‘def’)

Lists: Mutable
>>> li = [‘abc’, 23, 4.34, 23]
>>> li[1] = 45
>>> li
[‘abc’, 45, 4.34, 23]
• We can change lists in place.
• Name li still points to the same memory reference when we’re
done.
• The mutability of lists means that they aren’t as fast as tuples.

Operations on Lists Only 1
>>> li = [1, 11, 3, 4, 5]
>>> li.append(‘a’) # Our first exposure to method syntax
>>> li
[1, 11, 3, 4, 5, ‘a’]
>>> li.insert(2, ‘i’)
>>>li
[1, 11, ‘i’, 3, 4, 5, ‘a’]

The extend method vs the + operator.
• + creates a fresh list (with a new memory reference)
• extend operates on list li in place.
>>> li.extend([9, 8, 7])
>>>li
[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7]
Confusing:
• Extend takes a list as an argument.
• Append takes a singleton as an argument.
>>> li.append([10, 11, 12])
>>> li
[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7, [10, 11, 12]]

>>> li = [‘a’, ‘b’, ‘c’, ‘b’]
>>> li.index(‘b’) # index of first occurrence
1
>>> li.count(‘b’) # number of occurrences
2
>>> li.remove(‘b’) # remove first occurrence
>>> li
[‘a’, ‘c’, ‘b’]

>>> li = [5, 2, 6, 8]
>>> li.reverse() # reverse the list *in place*
>>> li
[8, 6, 2, 5]
>>> li.sort() # sort the list *in place*
>>> li
[2, 5, 6, 8]
>>> li.sort(some_function)
# sort in place using user-defined comparison

Tuples vs. Lists
• Lists slower but more powerful than tuples.
• Lists can be modified, and they have lots of handy operations we can
perform on them.
• Tuples are immutable and have fewer features.
• To convert between tuples and lists use the list() and tuple()
functions:
li = list(tu)
tu = tuple(li)

Dictionaries
63

Dictionaries: A Mapping type
• Dictionaries store a mapping between a set of keys
and a set of values.
• Keys can be any immutable type.
• Values can be any type
• A single dictionary can store values of different types
• You can define, modify, view, lookup, and delete
the key-value pairs in the dictionary.

Using dictionaries
>>> d = {‘user’:‘bozo’, ‘pswd’:1234}
>>> d[‘user’]
‘bozo’
>>> d[‘pswd’]
1234
>>> d[‘bozo’]
Traceback (innermost last):
File ‘<interactive input>’ line 1, in ?
KeyError: bozo
>>> d = {‘user’:‘bozo’, ‘pswd’:1234}
>>> d[‘user’] = ‘clown’
>>> d
{‘user’:‘clown’, ‘pswd’:1234}
>>> d[‘id’] = 45
>>> d
{‘user’:‘clown’, ‘id’:45, ‘pswd’:1234}
>>> d = {‘user’:‘bozo’, ‘p’:1234, ‘i’:34}
>>> del d[‘user’] # Remove one.
>>> d
{‘p’:1234, ‘i’:34}
>>> d.clear() # Remove all.
>>> d
{}
>>> d = {‘user’:‘bozo’, ‘p’:1234, ‘i’:34}
>>> d.keys() # List of keys.
[‘user’, ‘p’, ‘i’]
>>> d.values() # List of values.
[‘bozo’, 1234, 34]
>>> d.items() # List of item tuples.
[(‘user’,‘bozo’), (‘p’,1234), (‘i’,34)]

Functions

Functions
• def creates a function and assigns it a name
• return sends a result back to the caller
• Arguments are passed by assignment
• Arguments and return types are not declared
def <name>(arg1, arg2, ..., argN):
! <statements>
! return <value>
def times(x,y):
! return x*y

Passing Arguments to Functions
• Arguments are passed by assignment
• Passed arguments are assigned to local names
• Assignment to argument names don't affect the
caller
• Changing a mutable argument may affect the caller
def changer (x,y):
! x = 2! ! ! # changes local value of x only
! y[0] = 'hi'!! # changes shared object

Optional Arguments
• Can define defaults for arguments that need not be
passed
def func(a, b, c=10, d=100):
! print a, b, c, d
>>> func(1,2)
1 2 10 100
>>> func(1,2,3,4)
1,2,3,4

Gotchas
• All functions in Python have a return value
• even if no return line inside the code.
• Functions without a return return the special value
None.
• There is no function overloading in Python.
• Two different functions can’t have the same name, even if they
have different arguments.
• Functions can be used as any other data type.
They can be:
• Arguments to function
• Return values of functions
• Assigned to variables
• Parts of tuples, lists, etc

Control of Flow
71

Examples
if x == 3:
print “X equals 3.”
elif x == 2:
print “X equals 2.”
else:
print “X equals something else.”
print “This is outside the ‘if’.”
x = 3
while x < 10:
if x > 7:
x += 2
continue
x = x + 1
print “Still in the loop.”
if x == 8:
break
print “Outside of the loop.”
assert(number_of_players < 5)
for x in range(10):
if x > 7:
x += 2
continue
x = x + 1
print “Still in the loop.”
if x == 8:
break
print “Outside of the loop.”

Modules

Why Use Modules?
• Code reuse
• Routines can be called multiple times within a program
• Routines can be used from multiple programs
• Namespace partitioning
• Group data together with functions used for that data
• Implementing shared services or data
• Can provide global data structure that is accessed by multiple
subprograms

Modules
• Modules are functions and variables defined in
separate files
• Items are imported using from or import
from module import function
function()
import module
module.function()
• Modules are namespaces
• Can be used to organize variable names, i.e.
atom.position = atom.position - molecule.position

Classes and Objects

What is an Object?
• A software item that contains variables and
methods
• Object Oriented Design focuses on
• Encapsulation:
—dividing the code into a public interface, and a private implementation
of that interface
• Polymorphism:
—the ability to overload standard operators so that they have appropriate
behavior based on their context
• Inheritance:
—the ability to create subclasses that contain specializations of their
parents

Example
class atom(object):
! def __init__(self,atno,x,y,z):
! ! self.atno = atno
! ! self.position = (x,y,z)
! def symbol(self): # a class method
! ! return Atno_to_Symbol[atno]
! def __repr__(self): # overloads printing
! ! return '%d %10.4f %10.4f %10.4f' %
! ! ! (self.atno, self.position[0],
! ! ! self.position[1],self.position[2])
>>> at = atom(6,0.0,1.0,2.0)
>>> print at
6 0.0000 1.0000 2.0000
>>> at.symbol()
'C'

Atom Class
• Overloaded the default constructor
• Defined class variables (atno,position) that are
persistent and local to the atom object
• Good way to manage shared memory:
• instead of passing long lists of arguments, encapsulate some of
this data into an object, and pass the object.
• much cleaner programs result
• Overloaded the print operator
• We now want to use the atom class to build
molecules...

Molecule Class
class molecule:
! def __init__(self,name='Generic'):
! ! self.name = name
! ! self.atomlist = []
! def addatom(self,atom):
! ! self.atomlist.append(atom)
! def __repr__(self):
! ! str = 'This is a molecule named %sn' % self.name
! ! str = str+'It has %d atomsn' % len(self.atomlist)
! ! for atom in self.atomlist:
! ! ! str = str + `atom` + 'n'
! ! return str

Using Molecule Class
>>> mol = molecule('Water')
>>> at = atom(8,0.,0.,0.)
>>> mol.addatom(at)
>>> mol.addatom(atom(1,0.,0.,1.))
>>> mol.addatom(atom(1,0.,1.,0.))
>>> print mol
This is a molecule named Water
It has 3 atoms
8 0.000 0.000 0.000
1 0.000 0.000 1.000
1 0.000 1.000 0.000
• Note that the print function calls the atoms print
function
• Code reuse: only have to type the code that prints an atom
once; this means that if you change the atom specification, you
only have one place to update.

Inheritance
class qm_molecule(molecule):
def addbasis(self):
self.basis = []
for atom in self.atomlist:
self.basis = add_bf(atom,self.basis)
• __init__, __repr__, and __addatom__ are taken
from the parent class (molecule)
• Added a new function addbasis() to add a basis set
• Another example of code reuse
• Basic functions don't have to be retyped, just inherited
• Less to rewrite when specifications change

Overloading
def __repr__(self):
! ! str = 'QM Rules!n'
! ! for atom in self.atomlist:
! ! ! str = str + `atom` + 'n'
! ! return str
• Now we only inherit __init__ and addatom from the
parent
• We define a new version of __repr__ specially for
QM

Adding to Parent Functions
• Sometimes you want to extend, rather than
replace, the parent functions.
! def __init__(self,name="Generic",basis="6-31G**"):
! ! self.basis = basis
! ! super(qm_molecule, self).__init__(name)

Public and Private Data
• In Python anything with two leading underscores
is private
__a, __my_variable
• Anything with one leading underscore is semi-
private, and you should feel guilty accessing this
data directly.
_b
• Sometimes useful as an intermediate step to making data
private

The Extra Stuff...
86

File I/O, Strings, Exceptions...
fileptr = open(‘filename’)
somestring = fileptr.read()
for line in fileptr:
print line
fileptr.close()
>>> a = 1
>>> b = 2.4
>>> c = 'Tom'
>>> '%s has %d coins worth a total of $%.02f' % (c, a, b)
'Tom has 1 coins worth a total of $2.40'
>>> try:
... 1 / 0
... except:
... print('That was silly!')
... finally:
... print('This gets executed no matter what')
...
That was silly!
This gets executed no matter what

Python

More Related Content

What's hot (18)

Similar to Python (20)

Recently uploaded (20)

Python